No more typing reviews! Try our Samantha, our new voice AI agent.

Cohere vs Gemini Enterprise Agent Platform comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Apr 23, 2026

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Cohere
Ranking in AI Development Platforms
10th
Average Rating
7.8
Reviews Sentiment
6.8
Number of Reviews
10
Ranking in other categories
AI Writing Tools (3rd), Large Language Models (LLMs) (3rd), AI Proofreading Tools (3rd)
Gemini Enterprise Agent Pla...
Ranking in AI Development Platforms
1st
Average Rating
8.2
Reviews Sentiment
6.3
Number of Reviews
15
Ranking in other categories
AI-Agent Builders (5th)
 

Mindshare comparison

As of April 2026, in the AI Development Platforms category, the mindshare of Cohere is 1.8%, up from 0.5% compared to the previous year. The mindshare of Gemini Enterprise Agent Platform is 8.2%, down from 14.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Mindshare Distribution
ProductMindshare (%)
Google Vertex AI8.2%
Cohere1.8%
Other90.0%
AI Development Platforms
 

Featured Reviews

AS
Engineer at Roche
Have improved project workflows using faster response times and reduced data embedding costs
One thing that Cohere can improve is related to some distances when I am trying similarity search. Let's suppose I have provided textual data that has been embedded. I have to use some extra process from numpy after embedding the model. In the case of OpenAI embedding models, I do not have to use that extra process, and they provide lower distances compared to my results from Cohere. I was getting distances of approximately 0.005 sometimes, but in the case of Cohere, I was getting distances around 0.5 or sometimes more than that. I think that can be improved. It was possibly because of some configuration or the way I was using it, but I am not exactly sure about that.
Hamada Farag - PeerSpot reviewer
Technology Consultant at Beta Information Technology
Customization and integration empower diverse AI applications
We are familiar with most Google Cloud services, particularly infrastructure services, storage, compute, AI tools, containerization, GCP containerization, and cloud SQL. We are familiar with approximately eighty percent of Google's services, primarily related to infrastructure, AI, containers, backup, storage, and compute. We are familiar with Gemini AI and Google Vertex AI, and we have completed some exercises and cases with our customers for Google AI. We use automation in machine learning. I work with a team where everyone has specific responsibilities. We have design and development processes in place. Based on my experience, I would rate Google Vertex AI a 9 out of 10.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"I assess the value of Cohere's API support in my business operations as easy to integrate."
"A key advantage of integrating Cohere’s reranking model is that it aligns with client requests to include a reranking module — a widely recognized method for improving RAG quality. Additionally, the API demonstrates strong performance in terms of response speed."
"Cohere has positively impacted my organization by helping our customers work more efficiently when creating requests, and the embedding results are of very high quality."
"Cohere helped us with all three aspects: money is saved, time is saved, and we needed fewer resources to meet our end goals."
"Cohere positively impacted my organization by improving the performance of my RAG system."
"The best feature Cohere offers is the Reranking model."
"Cohere has helped my organization innovate and stay ahead in our industry as Cohere was better than Titan, and it helped us to secure the client's confidence and we moved from proof of concept to production."
"The very first thing that I really like about it is the support team. They're really available on Discord, and they answer all of your questions."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"The support is perfect and fantastic."
"The best feature of Google Vertex AI is the ease of use, along with the integration with the rest of the Google ecosystem and the way models can be made available outside Google through endpoints."
"The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"The features I have found most valuable in Google Vertex AI are Gemini's large language models, which are currently among the best, and the vision tool of Gemini, which I consider quite good."
"The most useful function of Google Vertex AI for me is the ease of integration, as we can easily create a prompt and integrate it into our current system."
"We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."
 

Cons

"I have not observed any measurable benefits or return on investment with Cohere."
"Cohere has text generation. I think it is mainly focused on AI search. If there was a way to combine the searches with images, I think it would be nice to include that."
"One thing that Cohere can improve is related to some distances when I am trying similarity search."
"I believe Cohere can be improved technically by providing more feedback, logs, and metrics for embedding requests, as it currently appears to be a black box without any understanding of quality."
"It's challenging for us to make a conclusion about quality enhancement by using reranking models, as solid evaluation methodology for reranking is still immature."
"The documentation and support could be improved, as there is limited documentation available on the web."
"Cohere can be improved by having more integrations beyond its current offerings with Amazon."
"Cohere has text generation. I think it is mainly focused on AI search. If there was a way to combine the searches with images, I think it would be nice to include that."
"I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
"Google can improve Google Vertex AI in terms of analysis and accuracy. When passing a very large context, instead of receiving vague responses, it would be better if the system could prompt users not to pass overly large prompts and provide clearer guidance on how to fine-tune Gemini for specific use cases."
"I think the technical documentation is not readily available in the tool."
"I'm not sure if I have suggestions for improvement."
"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."
"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."
 

Pricing and Cost Advice

Information not available
"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."
"The price structure is very clear"
"The solution's pricing is moderate."
"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."
report
Use our free recommendation engine to learn which AI Development Platforms solutions are best for your needs.
889,955 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
13%
Manufacturing Company
9%
Comms Service Provider
8%
Marketing Services Firm
8%
Financial Services Firm
10%
Computer Software Company
10%
Manufacturing Company
10%
Comms Service Provider
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise1
Large Enterprise7
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise4
Large Enterprise7
 

Questions from the Community

What is your experience regarding pricing and costs for Cohere?
My experience with pricing, setup cost, and licensing was that it was all managed by AWS, and we had AWS credits, so I did not have to dive into that.
What needs improvement with Cohere?
Cohere can be improved by having more integrations beyond its current offerings with Amazon. Integrations with Databricks, Azure, and Google Cloud would be beneficial.
What is your primary use case for Cohere?
My main use case for Cohere is that it's a good embedding model. I have used it with Titan, but Cohere came out better. A specific example of how I've used Cohere for embeddings is when I was worki...
What is your experience regarding pricing and costs for Google Vertex AI?
I purchased Google Vertex AI directly from Google, as we are a partner of Google. I would rate the pricing for Google Vertex AI as low; the price is affordable.
What needs improvement with Google Vertex AI?
Google Vertex AI is quite complex to navigate and to start services with, as I need to do a lot of iterations to finally activate the services, which is one major flaw, although it is powerful. To ...
What is your primary use case for Google Vertex AI?
Google Vertex AI has been utilized for Vertex Pipelines. I have not utilized the pre-trained APIs in Google Vertex AI, as our deployment is primarily on AWS, and we use API calls.
 

Also Known As

No data available
Vertex, Google Vertex AI
 

Overview

Find out what your peers are saying about Cohere vs. Gemini Enterprise Agent Platform and other solutions. Updated: April 2026.
889,955 professionals have used our research since 2012.